tidsseriemotivs

A time series motif is a subsequence within a time series that occurs repeatedly. Identifying these motifs is a fundamental task in time series data mining and analysis. The goal is to find patterns that are highly similar to each other within the same time series. These recurring patterns can represent important events, behaviors, or states within the data.

The concept of a motif is closely related to the idea of discords, which are subsequences that are very different from all others. Motifs, on the other hand, represent the most common or representative patterns. The definition of "similarity" is crucial in motif discovery and is typically measured using distance metrics such as Euclidean distance or Dynamic Time Warping (DTW). DTW is often preferred as it can account for time shifts and variations in speed within the subsequences.

Applications of time series motif discovery are diverse. In healthcare, motifs might represent recurring symptoms or physiological patterns. In finance, they could indicate typical market behaviors or trading strategies. In sensor networks, motifs could signify common operational states or anomalies. By identifying these repeated patterns, analysts can gain insights into the underlying processes generating the time series, build predictive models, or detect deviations from normal behavior. Algorithms for motif discovery aim to efficiently search through all possible subsequences and compare them to find the most frequent and similar ones.